Datamining, also know to geeks at KDD (Knowledge Discovery in Databases), is defined as "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data."
In plain terms, it is a way to use the information you have gleaned from your patrons to help you find new patrons, cut costs, or discover trends.
Datamining in Action
One midwest grocery chain used the data mining capacity of Oracle software to analyze local buying patterns. They discovered that when men bought diapers on Thursdays and Saturdays, they also tended to buy beer. Further analysis showed that these shoppers typically did their weekly grocery shopping on Saturdays. On Thursdays, however, they only bought a few items. The retailer concluded that they purchased the beer to have it available for the upcoming weekend. The grocery chain could use this newly discovered information in various ways to increase revenue. For example, they could move the beer display closer to the diaper display. And, they could make sure beer and diapers were sold at full price on Thursdays.
Blockbuster Entertainment, as well as Netflix, mines its video rental history database to recommend rentals to individual customers.
Amazon.com uses its huge database to tell you about other books you may be interested in purchased based on previous purchases--as well as other customer who have similar purchasing patterns.
American Express can suggest products to its cardholders based on analysis of their monthly expenditures.
Wal-mart is perhaps the King of Datamining, storying over 12 terrabytes of data regarding products and purchases, even weather issues, dates, and traffic patterns.
When Hurricane Francis was on its way, barreling across the Caribbean, threatening a direct hit on Florida's Atlantic coast. Linda M. Dillman, Wal-Mart's chief information officer, pressed her staff to come up with forecasts based on what had happened when Hurricane Charley struck several weeks earlier. Backed by the trillions of bytes' worth of shopper history that is stored in Wal-Mart's computer network, she felt that the company could "start predicting what's going to happen, instead of waiting for it to happen," as she put it.
The experts mined the data and found that the stores would indeed need certain products - and not just the usual flashlights. "We didn't know in the past that strawberry Pop-Tarts increase in sales, like seven times their normal sales rate, ahead of a hurricane," Ms. Tillman said in a recent interview. "And the pre-hurricane top-selling item was beer."
Thanks to those insights, trucks filled with toaster pastries and six-packs were soon speeding down Interstate 95 toward Wal-Marts in the path of Frances. Most of the products that were stocked for the storm sold quickly, and at full price.
How does this help the Arts?
Good arts marketers have been datamining for years, we just called it Niche or Targeted Marketing. With the proper set of data, for example a history of performances for a patron, Arts Marketers can utilize the data they have collected to make the sale.
Utilizing both the power of Data mining and the Internet--Arts Marketers can create targeted offers to patrons that would have been too time intensive in the past.
For example, what if I was tracking a customer named John Smith. The data tells me he has been to 3 performances in the last 2 years, he has purchased two tickets on each occasion, but the dates don't seem to show a logical grouping (July, May, November). So, we know that John is not a date-specific buyer.
Looking at the shows in those dates we notice no trend in genre (one is a Shakespeare, one is a modern Drama, another a musical) so we now know there is something else driving this customer into purchasing decisions.
Is it price? No, he is paying full price for every show. Is it seat location? No, he seems to sit at whatever is available. He does seem to order a good 2 weeks before the performance, which is unusual--but not enough to go on.
At this point, most Arts Marketers would be stumped. We simply do no have enough data to target a message.
But, let us assume that we are really good at what we do. Let us further assume that nobody enters our theatre without us getting their mailing list and information so that we not only have the address of who purchased the tickets, but the address of people they purchased tickets for!
A quick run on our data shows that Mr. Smith attends with a Maggie Smith, who lives in Arizona (500 miles away), and the answer becomes clear. John Smith only attends when his Mother comes to visit!
So now I know how to target John Smith. I need to send a message to Maggie Smith telling her about a great subscription package that her son would enjoy. It makes a great holiday gift, you know.